Beta Series Connectivity
Catherine Walsh
11/7/2020
This analysis looks at the beta series connectivity between the fronto-parietal control and default mode networks from the Yeo atlas, in addition to individually defined ROIs from the FFA and the anterior, medial and posterior HPC. Individual connectivity matrices are calculated for each task period (encoding, delay and probe).
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.1
## ✓ tidyr 1.1.1 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(rmatio)
library(patchwork)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithsload('data/behav.RData')
load('data/split_groups_info.RData')
source("helper_fxns/calc_network_avg.R")
source("helper_fxns/mutate_for_heatmap.R")
source("helper_fxns/calc_network_avg_matrix.R")
se <- function(x) {
sd(x,na.rm=TRUE)/sqrt(length(x[!is.na(x)]))
}Prep data
avg_beta_series <- list(cue_load1 = read.mat("data/BetaSeries/RcorrCue_load1_Acc1.mat")$R,
cue_load3 = read.mat("data/BetaSeries/RcorrCue_load3_Acc1.mat")$R,
delay_load1 = read.mat("data/BetaSeries/RcorrDelay_load1_Acc1.mat")$R,
delay_load3 = read.mat("data/BetaSeries/RcorrDelay_load3_Acc1.mat")$R,
probe_load1 = read.mat("data/BetaSeries/RcorrProbe_load1_Acc1.mat")$R,
probe_load3 = read.mat("data/BetaSeries/RcorrProbe_load3_Acc1.mat")$R)
rois <- read.mat("data/BetaSeries/roi_order.mat")$roi
beta_series_sujs <- unlist(read.mat("data/BetaSeries/suj_order.mat"))
beta_series_cond_order <- read.mat("data/BetaSeries/condition_order.mat")
suj_by_cond <- read.mat("data/BetaSeries/Suj_by_Cond.mat")$Suj_by_Condfor (cond in seq.int(1,6)){
avg_beta_series[[cond]] <- atanh(avg_beta_series[[cond]])
}
for (suj in seq.int(1,168)){
for (cond in seq.int(7,12)){
suj_by_cond[[suj]][[cond]] <- atanh(suj_by_cond[[suj]][[cond]])
}
}For most of these analyses, we are going to just look at the average connectivity across networks. In addition, however, we’re going to break the FPCN into more parietal regions and more PFC regions, and segment the hippocampus into anterior, medial and posterior segments.
avg_data <- data.frame(matrix(nrow=6, ncol=34))
colnames(avg_data) <- c("PTID","FPCN_FPCN", "DMN_DMN", "HPC_HPC", "FFA_FFA", "FPCN_DMN", "FPCN_HPC", "FPCN_FFA", "DMN_HPC",
"DMN_FFA", "HPC_FFA", "FPCN_PFC_FPCN_PFC", "FPCN_PFC_FPCN_Par", "FPCN_PFC_DMN", "FPCN_PFC_HPC",
"FPCN_PFC_FFA", "FPCN_Par_DMN", "FPCN_Par_HPC", "FPCN_Par_FFA", "FPCN_HPC_Ant", "FPCN_PFC_HPC_Ant",
"FPCN_Par_HPC_Ant", "DMN_HPC_Ant", "FFA_HPC_Ant","FPCN_HPC_Med", "FPCN_PFC_HPC_Med",
"FPCN_Par_HPC_Med", "DMN_HPC_Med", "FFA_HPC_Med","FPCN_HPC_Post", "FPCN_PFC_HPC_Post",
"FPCN_Par_HPC_Post", "DMN_HPC_Post", "FFA_HPC_Post")
for (cond in seq.int(1,6)){
avg_data[cond,] <- calc_network_avg(names(avg_beta_series)[cond],avg_beta_series[[cond]] )
}
suj_avg_data <- data.frame(matrix(nrow=169, ncol=34))
colnames(suj_avg_data) <- c("PTID","FPCN_FPCN", "DMN_DMN", "HPC_HPC", "FFA_FFA", "FPCN_DMN", "FPCN_HPC", "FPCN_FFA", "DMN_HPC",
"DMN_FFA", "HPC_FFA", "FPCN_PFC_FPCN_PFC", "FPCN_PFC_FPCN_Par", "FPCN_PFC_DMN", "FPCN_PFC_HPC",
"FPCN_PFC_FFA", "FPCN_Par_DMN", "FPCN_Par_HPC", "FPCN_Par_FFA", "FPCN_HPC_Ant", "FPCN_PFC_HPC_Ant",
"FPCN_Par_HPC_Ant", "DMN_HPC_Ant", "FFA_HPC_Ant","FPCN_HPC_Med", "FPCN_PFC_HPC_Med",
"FPCN_Par_HPC_Med", "DMN_HPC_Med", "FFA_HPC_Med","FPCN_HPC_Post", "FPCN_PFC_HPC_Post",
"FPCN_Par_HPC_Post", "DMN_HPC_Post", "FFA_HPC_Post")
suj_avg_list <- list(
cue_load3 = suj_avg_data,
delay_load3 = suj_avg_data,
probe_load3 = suj_avg_data,
cue_load1 = suj_avg_data,
delay_load1 = suj_avg_data,
probe_load1 = suj_avg_data,
cue_load_effect = suj_avg_data,
delay_load_effect = suj_avg_data,
probe_load_effect = suj_avg_data
)
for (cond in seq.int(7,12)){
for (suj in seq.int(1,169)){
if (suj != 55){
suj_avg_list[[cond-6]][suj,] <- calc_network_avg(beta_series_sujs[suj], suj_by_cond[[suj]][[cond]])
}else{
suj_avg_list[[cond-6]]$PTID[suj] <- beta_series_sujs[suj]
}
}
suj_avg_list[[cond-6]]$PTID <- c(suj_avg_list[[cond-6]]$PTID[1:54], 1554, suj_avg_list[[cond-6]]$PTID[55:168])
}
for (LE in seq.int(7,9)){
suj_avg_list[[LE]]$PTID <- suj_avg_list[[1]]$PTID
}suj_avg_list[["cue_load_effect"]][,2:34] <- suj_avg_list[["cue_load3"]][,2:34] - suj_avg_list[["cue_load1"]][,2:34]
suj_avg_list[["delay_load_effect"]][,2:34] <- suj_avg_list[["delay_load3"]][,2:34] - suj_avg_list[["delay_load1"]][,2:34]
suj_avg_list[["probe_load_effect"]][,2:34] <- suj_avg_list[["probe_load3"]][,2:34] - suj_avg_list[["probe_load1"]][,2:34]
avg_data[7,2:34] <- avg_data[2,2:34]- avg_data[1,2:34]
avg_data$PTID[7] <- "cue_load_effect"
avg_data[8,2:34] <- avg_data[4,2:34]- avg_data[3,2:34]
avg_data$PTID[8] <- "delay_load_effect"
avg_data[9,2:34] <- avg_data[6,2:34]- avg_data[5,2:34]
avg_data$PTID[9] <- "probe_load_effect"
avg_beta_series[["cue_load_effect"]] <- avg_beta_series[["cue_load3"]] - avg_beta_series[["cue_load1"]]
avg_beta_series[["delay_load_effect"]] <- avg_beta_series[["delay_load3"]] - avg_beta_series[["delay_load1"]]
avg_beta_series[["probe_load_effect"]] <- avg_beta_series[["probe_load3"]] - avg_beta_series[["probe_load1"]]check_span_groups <- constructs_fMRI
check_span_groups<- check_span_groups[order(check_span_groups$omnibus_span_no_DFR_MRI),]
check_span_groups$without_MRI <- "low"
check_span_groups$without_MRI[57] <- "not_incl"
check_span_groups$without_MRI[58:113] <- "med"
check_span_groups$without_MRI[114] <- "not_incl"
check_span_groups$without_MRI[115:170] <- "high"
check_span_groups<- check_span_groups[order(check_span_groups$omnibus_span_no_DFR),]
check_span_groups$without_EEG <- "low"
check_span_groups$without_EEG[57] <- "not_incl"
check_span_groups$without_EEG[58:113] <- "med"
check_span_groups$without_EEG[114] <- "not_incl"
check_span_groups$without_EEG[115:170] <- "high"
#sum(check_span_groups$without_MRI != check_span_groups$without_EEG, na.rm=TRUE)
check_span_groups <- merge(check_span_groups, p200_demographics)
colnames(check_span_groups)[10] <- "level"
WM_groups_no_EEG <- list(high = check_span_groups %>% filter(level == "high"),
med = check_span_groups %>% filter(level == "med"),
low = check_span_groups %>% filter(level == "low"))
WM_groups_no_EEG[["all"]] <- rbind(WM_groups_no_EEG[["low"]],WM_groups_no_EEG[["med"]],WM_groups_no_EEG[["high"]] )WM_merge <- merge(WM_groups_no_EEG[["all"]], constructs_fMRI)
for (cond in seq.int(1,9)){
suj_avg_list[[cond]] <- merge(suj_avg_list[[cond]], WM_merge, by = "PTID")
}for (cond in seq.int(4,9)){
for (col in seq.int(2,34)){
m <- mean(suj_avg_list[[cond]][,col], na.rm=TRUE)
s <- sd(suj_avg_list[[cond]][,col], na.rm=TRUE)
max <- m + 3*s
min <- m - 3*s
suj_avg_list[[cond]][[is.na(suj_avg_list[[cond]][,col]),col]] <- 999
suj_avg_list[[cond]][(suj_avg_list[[cond]][,col] < min | suj_avg_list[[cond]][,col] > max), col] <- NA
}
}High Load
Here, we are just looking at the connectivity across the task at high load. First, we’re going to just plot the connectivity matrices to get a sense of how things look at the network level.
Connectivity Matrices
networks <- c("FPCN", "FPCN_PFC", "FPCN_Par","DMN", "HPC", "HPC_Ant","HPC_Med", "HPC_Post", "FFA")
cond_list <- c("Cue", "Delay", "Probe")
for (cond in c(2,4,6)){
calc_network_avg_matrix(avg_beta_series[[cond]]) %>%
as_tibble() %>%
rowid_to_column("X") %>%
gather(key="Y", value="Z", -1) %>%
#mutate(Y=as.numeric(gsub("V","",Y))) %>%
ggplot()+
geom_tile(aes(x=X,y=Y, fill=Z))+
theme_classic()+
theme(aspect.ratio=1,
axis.line=element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(angle=45, hjust=1))+
scale_x_continuous(breaks = c(1:9), labels=networks)+
scale_fill_gradient(limits = c(-1,0))+
labs(x="Network 1", y="Network 2", fill = "Connectivity", title = cond_list[cond/2]) -> temp_plot
print(temp_plot)
}
Directly compare across task period
Next, let’s directly compare across task period. If we actually compare the values, we’re not seeing any difference across time.
avg_data %>%
select(PTID, FPCN_FPCN, DMN_DMN, HPC_HPC, FFA_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load3", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("Within Network Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_DMN, FPCN_HPC, FPCN_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load3", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("FPCN Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_DMN, DMN_HPC, DMN_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load3", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("DMN Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_HPC, DMN_HPC, HPC_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load3", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("HPC Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_HPC_Ant, DMN_HPC_Ant, FFA_HPC_Ant) %>%
melt(id.vars="PTID") %>%
filter(grepl("load3", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("Anterior HPC Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_HPC_Med, DMN_HPC_Med, FFA_HPC_Med) %>%
melt(id.vars="PTID") %>%
filter(grepl("load3", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("Medial HPC Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_HPC_Post, DMN_HPC_Post, FFA_HPC_Post) %>%
melt(id.vars="PTID") %>%
filter(grepl("load3", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("Posterior HPC Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_FFA, DMN_FFA, HPC_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load3", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("FFA Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
for (roi in seq.int(2,34)){
temp_data <- data.frame(PTID = suj_avg_list[[1]]$PTID,
cue = suj_avg_list[["cue_load3"]][,roi],
delay = suj_avg_list[["probe_load3"]][,roi],
probe = suj_avg_list[["probe_load3"]][,roi])
temp_data <- melt(temp_data, id.vars = "PTID")
colnames(temp_data) <- c("PTID", "task_period", "connectivity")
print(colnames(suj_avg_list[[1]][roi]))
print(summary(aov(connectivity ~ task_period + Error(PTID), data = temp_data)))
}## [1] "FPCN_FPCN"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.2389 0.2389
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.05 0.02446 0.366 0.694
## Residuals 494 33.00 0.06680
## [1] "DMN_DMN"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.2881 0.2881
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.007 0.00350 0.071 0.931
## Residuals 494 24.308 0.04921
## [1] "HPC_HPC"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.007505 0.007505
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.76 0.3795 0.792 0.454
## Residuals 494 236.76 0.4793
## [1] "FFA_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.0903 0.0903
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 14 6.991 0.651 0.522
## Residuals 494 5307 10.743
## [1] "FPCN_DMN"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.353 0.353
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.015 0.00768 0.125 0.882
## Residuals 494 30.311 0.06136
## [1] "FPCN_HPC"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.5647 0.5647
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.03 0.01283 0.185 0.831
## Residuals 494 34.18 0.06920
## [1] "FPCN_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.06543 0.06543
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00028 0.003 0.997
## Residuals 494 46.68 0.09449
## [1] "DMN_HPC"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.07368 0.07368
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.052 0.02576 0.431 0.65
## Residuals 494 29.528 0.05977
## [1] "DMN_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.1961 0.1961
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.36 0.18211 1.86 0.157
## Residuals 494 48.37 0.09792
## [1] "HPC_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.2983 0.2983
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00189 0.012 0.988
## Residuals 494 74.71 0.15124
## [1] "FPCN_PFC_FPCN_PFC"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.2085 0.2085
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.09 0.04373 0.548 0.578
## Residuals 494 39.40 0.07977
## [1] "FPCN_PFC_FPCN_Par"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.2977 0.2977
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.07 0.03426 0.476 0.622
## Residuals 494 35.58 0.07203
## [1] "FPCN_PFC_DMN"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.3616 0.3616
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.01 0.00497 0.075 0.928
## Residuals 494 32.60 0.06599
## [1] "FPCN_PFC_HPC"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.5115 0.5115
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.03 0.01475 0.208 0.813
## Residuals 494 35.10 0.07105
## [1] "FPCN_PFC_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.1236 0.1236
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.0 0.00007 0.001 0.999
## Residuals 494 52.5 0.10628
## [1] "FPCN_Par_DMN"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.3916 0.3916
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.05 0.02611 0.362 0.696
## Residuals 494 35.62 0.07210
## [1] "FPCN_Par_HPC"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.9424 0.9424
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.06 0.03240 0.338 0.713
## Residuals 494 47.36 0.09587
## [1] "FPCN_Par_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.04776 0.04776
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.01 0.00458 0.034 0.966
## Residuals 494 65.91 0.13342
## [1] "FPCN_HPC_Ant"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.6632 0.6632
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.05 0.02458 0.236 0.79
## Residuals 494 51.45 0.10415
## [1] "FPCN_PFC_HPC_Ant"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.5194 0.5194
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.08 0.03992 0.34 0.712
## Residuals 494 58.04 0.11750
## [1] "FPCN_Par_HPC_Ant"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 1.298 1.298
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.12 0.06009 0.42 0.657
## Residuals 494 70.69 0.14310
## [1] "DMN_HPC_Ant"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.2937 0.2937
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.06 0.02867 0.286 0.751
## Residuals 494 49.55 0.10030
## [1] "FFA_HPC_Ant"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.8864 0.8864
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.0 0.00211 0.009 0.991
## Residuals 494 109.9 0.22246
## [1] "FPCN_HPC_Med"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.9614 0.9614
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.04 0.01903 0.201 0.818
## Residuals 494 46.66 0.09446
## [1] "FPCN_PFC_HPC_Med"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.8535 0.8535
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.03 0.01515 0.15 0.861
## Residuals 494 49.84 0.10088
## [1] "FPCN_Par_HPC_Med"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 1.623 1.623
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.10 0.05244 0.427 0.652
## Residuals 494 60.61 0.12270
## [1] "DMN_HPC_Med"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.1566 0.1566
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.03 0.01549 0.232 0.793
## Residuals 494 33.04 0.06689
## [1] "FFA_HPC_Med"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.3276 0.3276
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00006 0 1
## Residuals 494 97.55 0.19747
## [1] "FPCN_HPC_Post"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.2241 0.2241
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00002 0 1
## Residuals 494 49.58 0.10036
## [1] "FPCN_PFC_HPC_Post"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.2404 0.2404
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00027 0.002 0.998
## Residuals 494 53.48 0.10826
## [1] "FPCN_Par_HPC_Post"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.2976 0.2976
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00004 0 1
## Residuals 494 73.15 0.14807
## [1] "DMN_HPC_Post"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.002935 0.002935
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.10 0.05209 0.568 0.567
## Residuals 494 45.29 0.09168
## [1] "FFA_HPC_Post"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.03597 0.03597
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.02 0.01182 0.048 0.953
## Residuals 494 121.58 0.24611Split by groups
If we plot by WM capacity group, we’re not seeing any significant differences either.
plot_list_L3 <- list()
for (cond in seq.int(7,9)){
cond_list <- list()
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_FPCN", "DMN_DMN", "HPC_HPC", "FFA_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["within"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_DMN", "FPCN_HPC", "FPCN_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["FPCN"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_DMN", "DMN_HPC","DMN_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["DMN"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_HPC", "DMN_HPC","HPC_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["HPC"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_HPC_Ant", "DMN_HPC_Ant","FFA_HPC_Ant")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["HPC_Ant"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_HPC_Med", "DMN_HPC_Med","FFA_HPC_Med")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["HPC_Med"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_HPC_Post", "DMN_HPC_Post","FFA_HPC_Post")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "Post", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["HPC_Post"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_HPC", "DMN_HPC","HPC_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["HPC"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_FFA", "DMN_FFA","HPC_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["FFA"]]
plot_list_L3[[names(suj_avg_list)[cond]]] <- cond_list
}
plot_list_L3[["cue_load_effect"]][["within"]] + plot_list_L3[["delay_load_effect"]][["within"]] + plot_list_L3[["probe_load_effect"]][["within"]] +
plot_layout(guides="collect")+
plot_annotation(title = "Within Network Connectivity")
plot_list_L3[["cue_load_effect"]][["FPCN"]] + plot_list_L3[["delay_load_effect"]][["FPCN"]] + plot_list_L3[["probe_load_effect"]][["FPCN"]] +
plot_layout(guides="collect")+
plot_annotation(title = "FPCN Connectivity")
plot_list_L3[["cue_load_effect"]][["DMN"]] + plot_list_L3[["delay_load_effect"]][["DMN"]] +
plot_list_L3[["probe_load_effect"]][["DMN"]] +
plot_layout(guides="collect")+
plot_annotation(title = "DMN Connectivity")
plot_list_L3[["cue_load_effect"]][["HPC"]] + plot_list_L3[["delay_load_effect"]][["HPC"]] +
plot_list_L3[["probe_load_effect"]][["HPC"]] +
plot_layout(guides="collect")+
plot_annotation(title = "HPC Connectivity")
plot_list_L3[["cue_load_effect"]][["HPC_Ant"]] + plot_list_L3[["delay_load_effect"]][["HPC_Ant"]] +
plot_list_L3[["probe_load_effect"]][["HPC_Ant"]] +
plot_layout(guides="collect")+
plot_annotation(title = "Anterior HPC Connectivity")
plot_list_L3[["cue_load_effect"]][["HPC_Med"]] + plot_list_L3[["delay_load_effect"]][["HPC_Med"]] +
plot_list_L3[["probe_load_effect"]][["HPC_Med"]] +
plot_layout(guides="collect")+
plot_annotation(title = "Medial HPC Connectivity")
plot_list_L3[["cue_load_effect"]][["HPC_Post"]] + plot_list_L3[["delay_load_effect"]][["HPC_Post"]] +
plot_list_L3[["probe_load_effect"]][["HPC_Post"]] +
plot_layout(guides="collect")+
plot_annotation(title = "Posterior HPC Connectivity")
plot_list_L3[["cue_load_effect"]][["FFA"]] + plot_list_L3[["delay_load_effect"]][["FFA"]] +
plot_list_L3[["probe_load_effect"]][["FFA"]] +
plot_layout(guides="collect")+
plot_annotation(title = "FFA Connectivity")
for (cond in seq.int(1,3)){
print(names(suj_avg_list)[cond])
anova_results <- purrr::map(suj_avg_list[[cond]][,2:34], ~aov(.x ~ suj_avg_list[[cond]]$level ))
for (measure in seq.int(1,33)){
print(colnames(suj_avg_list[[cond]])[measure+1])
print(summary(anova_results[[measure]]))
}
}## [1] "cue_load3"
## [1] "FPCN_FPCN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.074 0.03717 0.419 0.658
## Residuals 163 14.446 0.08863
## 1 observation deleted due to missingness
## [1] "DMN_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.033 0.01672 0.317 0.729
## Residuals 163 8.602 0.05277
## 1 observation deleted due to missingness
## [1] "HPC_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.60 0.2980 0.707 0.495
## Residuals 163 68.68 0.4214
## 1 observation deleted due to missingness
## [1] "FFA_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 10.1 5.056 0.517 0.598
## Residuals 163 1595.5 9.788
## 1 observation deleted due to missingness
## [1] "FPCN_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.017 0.00848 0.121 0.886
## Residuals 163 11.410 0.07000
## 1 observation deleted due to missingness
## [1] "FPCN_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.039 0.01942 0.272 0.762
## Residuals 163 11.638 0.07140
## 1 observation deleted due to missingness
## [1] "FPCN_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.058 0.02918 0.233 0.792
## Residuals 163 20.403 0.12517
## 1 observation deleted due to missingness
## [1] "DMN_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.004 0.00179 0.032 0.969
## Residuals 163 9.139 0.05606
## 1 observation deleted due to missingness
## [1] "DMN_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.057 0.02856 0.287 0.751
## Residuals 163 16.212 0.09946
## 1 observation deleted due to missingness
## [1] "HPC_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.024 0.01188 0.095 0.909
## Residuals 163 20.376 0.12501
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_FPCN_PFC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.26 0.12998 1.318 0.271
## Residuals 163 16.07 0.09861
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_FPCN_Par"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.057 0.02839 0.305 0.737
## Residuals 163 15.167 0.09305
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.024 0.01182 0.162 0.851
## Residuals 163 11.921 0.07314
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.067 0.03358 0.432 0.65
## Residuals 163 12.676 0.07777
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.021 0.01075 0.079 0.924
## Residuals 163 22.231 0.13639
## 1 observation deleted due to missingness
## [1] "FPCN_Par_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.013 0.00643 0.089 0.915
## Residuals 163 11.784 0.07229
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.037 0.01838 0.235 0.791
## Residuals 163 12.733 0.07812
## 1 observation deleted due to missingness
## [1] "FPCN_Par_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.14 0.0701 0.48 0.619
## Residuals 163 23.78 0.1459
## 1 observation deleted due to missingness
## [1] "FPCN_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.005 0.00233 0.021 0.98
## Residuals 163 18.371 0.11271
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.021 0.01047 0.084 0.92
## Residuals 163 20.391 0.12510
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.033 0.01665 0.126 0.882
## Residuals 163 21.617 0.13262
## 1 observation deleted due to missingness
## [1] "DMN_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.008 0.00381 0.046 0.955
## Residuals 163 13.380 0.08209
## 1 observation deleted due to missingness
## [1] "FFA_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.127 0.06338 0.344 0.709
## Residuals 163 29.992 0.18400
## 1 observation deleted due to missingness
## [1] "FPCN_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.069 0.0344 0.292 0.748
## Residuals 163 19.234 0.1180
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.113 0.05667 0.449 0.639
## Residuals 163 20.563 0.12615
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.07 0.03486 0.275 0.76
## Residuals 163 20.63 0.12658
## 1 observation deleted due to missingness
## [1] "DMN_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.00 0.00011 0.002 0.998
## Residuals 163 11.43 0.07012
## 1 observation deleted due to missingness
## [1] "FFA_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.141 0.07044 0.393 0.675
## Residuals 163 29.192 0.17909
## 1 observation deleted due to missingness
## [1] "FPCN_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.112 0.0558 0.437 0.647
## Residuals 163 20.808 0.1277
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.129 0.06458 0.465 0.629
## Residuals 163 22.622 0.13878
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.11 0.0550 0.409 0.665
## Residuals 163 21.94 0.1346
## 1 observation deleted due to missingness
## [1] "DMN_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.02 0.00989 0.111 0.895
## Residuals 163 14.57 0.08940
## 1 observation deleted due to missingness
## [1] "FFA_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.19 0.09411 0.468 0.627
## Residuals 163 32.79 0.20115
## 1 observation deleted due to missingness
## [1] "delay_load3"
## [1] "FPCN_FPCN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.009 0.00450 0.076 0.927
## Residuals 163 9.622 0.05903
## 1 observation deleted due to missingness
## [1] "DMN_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.056 0.02818 0.573 0.565
## Residuals 163 8.018 0.04919
## 1 observation deleted due to missingness
## [1] "HPC_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.41 0.2059 0.521 0.595
## Residuals 163 64.45 0.3954
## 1 observation deleted due to missingness
## [1] "FFA_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 41 20.50 1.932 0.148
## Residuals 163 1729 10.61
## 1 observation deleted due to missingness
## [1] "FPCN_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.019 0.00954 0.144 0.866
## Residuals 163 10.777 0.06611
## 1 observation deleted due to missingness
## [1] "FPCN_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.079 0.03954 0.565 0.57
## Residuals 163 11.416 0.07003
## 1 observation deleted due to missingness
## [1] "FPCN_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.074 0.03697 0.485 0.617
## Residuals 163 12.428 0.07624
## 1 observation deleted due to missingness
## [1] "DMN_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.056 0.02802 0.523 0.594
## Residuals 163 8.725 0.05353
## 1 observation deleted due to missingness
## [1] "DMN_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.149 0.07446 0.851 0.429
## Residuals 163 14.270 0.08755
## 1 observation deleted due to missingness
## [1] "HPC_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.163 0.08129 0.79 0.456
## Residuals 163 16.778 0.10293
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_FPCN_PFC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.014 0.00715 0.099 0.905
## Residuals 163 11.722 0.07191
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_FPCN_Par"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.029 0.01430 0.244 0.784
## Residuals 163 9.541 0.05853
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.014 0.00675 0.093 0.911
## Residuals 163 11.814 0.07248
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.091 0.04550 0.605 0.547
## Residuals 163 12.253 0.07517
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.112 0.05597 0.692 0.502
## Residuals 163 13.176 0.08084
## 1 observation deleted due to missingness
## [1] "FPCN_Par_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.026 0.01292 0.19 0.827
## Residuals 163 11.065 0.06788
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.056 0.02793 0.366 0.694
## Residuals 163 12.441 0.07632
## 1 observation deleted due to missingness
## [1] "FPCN_Par_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.103 0.05135 0.596 0.552
## Residuals 163 14.045 0.08616
## 1 observation deleted due to missingness
## [1] "FPCN_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.155 0.07734 1.068 0.346
## Residuals 163 11.807 0.07244
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.172 0.0861 1.091 0.338
## Residuals 163 12.861 0.0789
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.092 0.04575 0.573 0.565
## Residuals 163 13.027 0.07992
## 1 observation deleted due to missingness
## [1] "DMN_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.123 0.06159 1.086 0.34
## Residuals 163 9.246 0.05673
## 1 observation deleted due to missingness
## [1] "FFA_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.443 0.2214 1.826 0.164
## Residuals 163 19.762 0.1212
## 1 observation deleted due to missingness
## [1] "FPCN_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.189 0.09446 1.148 0.32
## Residuals 163 13.414 0.08230
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.201 0.10036 1.113 0.331
## Residuals 163 14.698 0.09017
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.143 0.07146 0.797 0.452
## Residuals 163 14.609 0.08962
## 1 observation deleted due to missingness
## [1] "DMN_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.121 0.06067 1.045 0.354
## Residuals 163 9.464 0.05806
## 1 observation deleted due to missingness
## [1] "FFA_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.058 0.02881 0.223 0.8
## Residuals 163 21.063 0.12922
## 1 observation deleted due to missingness
## [1] "FPCN_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.007 0.00352 0.038 0.962
## Residuals 163 14.999 0.09202
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.024 0.01182 0.116 0.891
## Residuals 163 16.650 0.10215
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.002 0.00082 0.008 0.992
## Residuals 163 16.468 0.10103
## 1 observation deleted due to missingness
## [1] "DMN_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.006 0.00279 0.036 0.965
## Residuals 163 12.655 0.07764
## 1 observation deleted due to missingness
## [1] "FFA_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.066 0.03301 0.24 0.787
## Residuals 163 22.376 0.13728
## 1 observation deleted due to missingness
## [1] "probe_load3"
## [1] "FPCN_FPCN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.018 0.00907 0.158 0.854
## Residuals 163 9.341 0.05730
## 1 observation deleted due to missingness
## [1] "DMN_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.105 0.05242 1.085 0.34
## Residuals 163 7.875 0.04831
## 1 observation deleted due to missingness
## [1] "HPC_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.05 0.0270 0.053 0.949
## Residuals 163 83.69 0.5135
## 1 observation deleted due to missingness
## [1] "FFA_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 17.3 8.658 0.77 0.465
## Residuals 163 1833.5 11.248
## 1 observation deleted due to missingness
## [1] "FPCN_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.089 0.04448 0.761 0.469
## Residuals 163 9.529 0.05846
## 1 observation deleted due to missingness
## [1] "FPCN_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.122 0.06101 0.871 0.42
## Residuals 163 11.414 0.07002
## 1 observation deleted due to missingness
## [1] "FPCN_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.028 0.01378 0.171 0.843
## Residuals 163 13.115 0.08046
## 1 observation deleted due to missingness
## [1] "DMN_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.148 0.07385 1.194 0.306
## Residuals 163 10.082 0.06185
## 1 observation deleted due to missingness
## [1] "DMN_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.004 0.00224 0.023 0.978
## Residuals 163 16.145 0.09905
## 1 observation deleted due to missingness
## [1] "HPC_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.212 0.1059 0.637 0.53
## Residuals 163 27.093 0.1662
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_FPCN_PFC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.027 0.01332 0.187 0.83
## Residuals 163 11.613 0.07124
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_FPCN_Par"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.032 0.01605 0.254 0.776
## Residuals 163 10.296 0.06316
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.11 0.05492 0.861 0.425
## Residuals 163 10.40 0.06380
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.128 0.06411 0.924 0.399
## Residuals 163 11.306 0.06936
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.059 0.02957 0.319 0.728
## Residuals 163 15.127 0.09280
## 1 observation deleted due to missingness
## [1] "FPCN_Par_DMN"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.05 0.02520 0.341 0.712
## Residuals 163 12.06 0.07396
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.034 0.01708 0.157 0.855
## Residuals 163 17.733 0.10879
## 1 observation deleted due to missingness
## [1] "FPCN_Par_FFA"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.122 0.06082 0.474 0.623
## Residuals 163 20.896 0.12819
## 1 observation deleted due to missingness
## [1] "FPCN_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.068 0.03413 0.331 0.719
## Residuals 163 16.802 0.10308
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.049 0.02452 0.21 0.811
## Residuals 163 19.026 0.11673
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.021 0.01071 0.069 0.933
## Residuals 163 25.148 0.15428
## 1 observation deleted due to missingness
## [1] "DMN_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.071 0.03527 0.317 0.729
## Residuals 163 18.158 0.11140
## 1 observation deleted due to missingness
## [1] "FFA_HPC_Ant"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 1.49 0.7440 3.122 0.0467 *
## Residuals 163 38.84 0.2383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
## [1] "FPCN_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.323 0.16151 1.903 0.152
## Residuals 163 13.837 0.08489
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.343 0.17150 1.906 0.152
## Residuals 163 14.664 0.08997
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.195 0.09733 0.771 0.464
## Residuals 163 20.574 0.12622
## 1 observation deleted due to missingness
## [1] "DMN_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.322 0.16085 2.482 0.0867 .
## Residuals 163 10.564 0.06481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
## [1] "FFA_HPC_Med"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.15 0.07388 0.353 0.703
## Residuals 163 34.12 0.20935
## 1 observation deleted due to missingness
## [1] "FPCN_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.101 0.05071 0.576 0.563
## Residuals 163 14.340 0.08798
## 1 observation deleted due to missingness
## [1] "FPCN_PFC_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.153 0.07647 0.813 0.445
## Residuals 163 15.333 0.09406
## 1 observation deleted due to missingness
## [1] "FPCN_Par_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.001 0.00048 0.003 0.997
## Residuals 163 25.694 0.15763
## 1 observation deleted due to missingness
## [1] "DMN_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.16 0.08012 0.86 0.425
## Residuals 163 15.19 0.09319
## 1 observation deleted due to missingness
## [1] "FFA_HPC_Post"
## Df Sum Sq Mean Sq F value Pr(>F)
## suj_avg_list[[cond]]$level 2 0.01 0.00721 0.027 0.974
## Residuals 163 44.30 0.27181
## 1 observation deleted due to missingnessLoad Effect
Looking at load effect is more in line with the rest of our fMRI analyses, so let’s look at those. Again, we’re first going to plot the entire network connectivity matrix, to get a sense of the networks as a whole.
Connectivity matrix
for (cond in seq.int(7,9)){
calc_network_avg_matrix(avg_beta_series[[cond]]) %>%
as_tibble() %>%
rowid_to_column("X") %>%
gather(key="Y", value="Z", -1) %>%
ggplot()+
geom_tile(aes(x=X,y=Y, fill=Z))+
theme_classic()+
theme(aspect.ratio=1,
axis.line=element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(angle=45, hjust=1))+
scale_x_continuous(breaks = c(1:9), labels=networks)+
scale_fill_gradient(limits = c(-0.075,0.15))+
labs(x="Network 1", y="Network 2", fill = "Connectivity", title = cond_list[cond-6]) -> temp_plot
print(temp_plot)
}
Overall, we’re seeing load effects that are significantly different from zero in the parietal regions of the FPCN with the HPC (particularly the medial region) and the FFA during cue, and the FPCN and FFA with the anterior HPC, and the parietal regions of the FPCN with the anterior regions of the HPC during delay. No regions showed a significant overall load effect in the probe period.
for (cond in seq.int(7,9)){
print(names(suj_avg_list)[cond])
for (roi in seq.int(2,34)){
ttest_res <- (t.test(suj_avg_list[[cond]][,roi]))
if (ttest_res$p.value < 0.05){
print(colnames(suj_avg_list[[cond]])[roi])
}
}
}## [1] "cue_load_effect"
## [1] "FPCN_Par_FFA"
## [1] "FPCN_Par_HPC_Med"
## [1] "delay_load_effect"
## [1] "FPCN_HPC_Ant"
## [1] "FPCN_PFC_HPC_Ant"
## [1] "FPCN_Par_HPC_Ant"
## [1] "FFA_HPC_Ant"
## [1] "probe_load_effect"
## [1] "HPC_HPC"Directly compare across task period
As before, we can then directly compare across time periods. We’re not seeing anything too interesting here.
avg_data %>%
select(PTID, FPCN_FPCN, DMN_DMN, HPC_HPC, FFA_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load_effect", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("Within Network Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_DMN, FPCN_HPC, FPCN_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load_effect", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("FPCN Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_PFC_FPCN_Par, FPCN_PFC_DMN, FPCN_PFC_HPC, FPCN_PFC_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load_effect", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("FPCN PFC region connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_PFC_FPCN_Par, FPCN_Par_DMN, FPCN_Par_HPC, FPCN_Par_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load_effect", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("FPCN Parietal region connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_DMN, DMN_HPC, DMN_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load_effect", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("DMN Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_HPC, DMN_HPC, HPC_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load_effect", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("HPC Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_HPC_Ant, DMN_HPC_Ant, FFA_HPC_Ant) %>%
melt(id.vars="PTID") %>%
filter(grepl("load_effect", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("Anterior HPC Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_HPC_Med, DMN_HPC_Med, FFA_HPC_Med) %>%
melt(id.vars="PTID") %>%
filter(grepl("load_effect", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("Medial HPC Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_HPC_Post, DMN_HPC_Post, FFA_HPC_Post) %>%
melt(id.vars="PTID") %>%
filter(grepl("load_effect", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("Posterior HPC Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
avg_data %>%
select(PTID, FPCN_FFA, DMN_FFA, HPC_FFA) %>%
melt(id.vars="PTID") %>%
filter(grepl("load_effect", PTID)) %>%
ggplot(aes(x=variable, y =value, fill = PTID))+
geom_bar(stat="identity", position = "dodge")+
ggtitle("FFA Connectivity")+
ylab("Connectivity")+
xlab("Network")+
theme_classic()
for (roi in seq.int(2,34)){
temp_data <- data.frame(
PTID = suj_avg_list[[1]]$PTID,
cue = suj_avg_list[["cue_load_effect"]][,roi],
delay = suj_avg_list[["probe_load_effect"]][,roi],
probe = suj_avg_list[["probe_load_effect"]][,roi])
temp_data <- melt(temp_data, id.vars="PTID")
colnames(temp_data) <- c("PTID", "task_period", "connectivity")
print(colnames(suj_avg_list[[1]][roi]))
print(summary(aov(connectivity ~ task_period + Error(PTID), data = temp_data)))
}## [1] "FPCN_FPCN"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.01024 0.01024
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.043 0.02139 0.535 0.586
## Residuals 482 19.287 0.04002
## [1] "DMN_DMN"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.278 0.278
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00000 0 1
## Residuals 488 21.73 0.04452
## [1] "HPC_HPC"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 3.712 3.712
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 1.2 0.5918 0.847 0.429
## Residuals 490 342.5 0.6990
## [1] "FFA_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 7.188 7.188
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 1 0.356 0.019 0.981
## Residuals 494 9067 18.354
## [1] "FPCN_DMN"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.09637 0.09637
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.003 0.00137 0.032 0.968
## Residuals 483 20.373 0.04218
## [1] "FPCN_HPC"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.1285 0.1285
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.021 0.01039 0.175 0.839
## Residuals 484 28.725 0.05935
## [1] "FPCN_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.1091 0.1091
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.008 0.00399 0.062 0.94
## Residuals 483 30.956 0.06409
## [1] "DMN_HPC"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.01914 0.01914
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.002 0.00119 0.024 0.976
## Residuals 481 23.687 0.04925
## [1] "DMN_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.3901 0.3901
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.03 0.01727 0.241 0.786
## Residuals 485 34.69 0.07152
## [1] "HPC_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.3782 0.3782
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.03 0.01349 0.138 0.871
## Residuals 484 47.44 0.09803
## [1] "FPCN_PFC_FPCN_PFC"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.003382 0.003382
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.06 0.03135 0.47 0.626
## Residuals 487 32.51 0.06676
## [1] "FPCN_PFC_FPCN_Par"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.0127 0.0127
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.029 0.01442 0.297 0.744
## Residuals 482 23.437 0.04863
## [1] "FPCN_PFC_DMN"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.09221 0.09221
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.002 0.00081 0.017 0.983
## Residuals 486 23.774 0.04892
## [1] "FPCN_PFC_HPC"
##
## Error: PTID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 1 0.05142 0.05142
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.01 0.00642 0.093 0.911
## Residuals 488 33.82 0.06930
## [1] "FPCN_PFC_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.308 0.308
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00038 0.004 0.996
## Residuals 485 41.66 0.08590
## [1] "FPCN_Par_DMN"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.06493 0.06493
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.006 0.00303 0.058 0.943
## Residuals 485 25.100 0.05175
## [1] "FPCN_Par_HPC"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.2941 0.2941
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.05 0.02410 0.318 0.728
## Residuals 483 36.64 0.07587
## [1] "FPCN_Par_FFA"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.07465 0.07465
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.04 0.02243 0.326 0.722
## Residuals 481 33.04 0.06869
## [1] "FPCN_HPC_Ant"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.3798 0.3798
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.01 0.00417 0.048 0.953
## Residuals 481 41.42 0.08611
## [1] "FPCN_PFC_HPC_Ant"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.4467 0.4467
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.03 0.0162 0.156 0.855
## Residuals 486 50.40 0.1037
## [1] "FPCN_Par_HPC_Ant"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.3831 0.3831
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.01 0.00703 0.061 0.941
## Residuals 483 55.88 0.11569
## [1] "DMN_HPC_Ant"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.3049 0.3049
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00003 0 1
## Residuals 483 44.43 0.09199
## [1] "FFA_HPC_Ant"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.8736 0.8736
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.06 0.03224 0.211 0.81
## Residuals 483 73.71 0.15261
## [1] "FPCN_HPC_Med"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.4108 0.4108
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.09 0.04562 0.649 0.523
## Residuals 486 34.18 0.07032
## [1] "FPCN_PFC_HPC_Med"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.4141 0.4141
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00102 0.012 0.988
## Residuals 489 40.56 0.08295
## [1] "FPCN_Par_HPC_Med"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.2592 0.2592
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.06 0.02933 0.341 0.712
## Residuals 484 41.67 0.08610
## [1] "DMN_HPC_Med"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.1176 0.1176
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00000 0 1
## Residuals 486 31.22 0.06423
## [1] "FFA_HPC_Med"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.5854 0.5854
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.00 0.00073 0.006 0.994
## Residuals 483 55.36 0.11461
## [1] "FPCN_HPC_Post"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.0207 0.0207
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.08 0.03865 0.455 0.635
## Residuals 484 41.14 0.08500
## [1] "FPCN_PFC_HPC_Post"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.00448 0.00448
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.16 0.08039 0.746 0.475
## Residuals 486 52.38 0.10778
## [1] "FPCN_Par_HPC_Post"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.02131 0.02131
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.04 0.02203 0.218 0.804
## Residuals 482 48.74 0.10112
## [1] "DMN_HPC_Post"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.02108 0.02108
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.02 0.00968 0.143 0.867
## Residuals 481 32.61 0.06779
## [1] "FFA_HPC_Post"
##
## Error: PTID
## Df Sum Sq Mean Sq
## task_period 1 0.2434 0.2434
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## task_period 2 0.05 0.02358 0.156 0.856
## Residuals 484 73.29 0.15142By WM group
Now, let’s look across WM groups (which we care more about anyways). Here, we see differences within HPC in the cue, for the DMN/FFA (low > medium), HPC/FFA (low > medium), parietal regions of the FPCN/DMN (low > medium, with high > medium trending), FFA/anterior HPC regions (low > medium and high) during delay, and parietal regions of the FPCN/FFA (high > low), FFA/posterior HPC (high > low) in probe.
plot_list_load_effect <- list()
for (cond in seq.int(7,9)){
cond_list <- list()
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_FPCN", "DMN_DMN", "HPC_HPC", "FFA_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["within"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_DMN", "FPCN_HPC", "FPCN_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["FPCN"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_DMN", "DMN_HPC","DMN_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["DMN"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_HPC", "DMN_HPC","HPC_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["HPC"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_HPC_Ant", "DMN_HPC_Ant","FFA_HPC_Ant")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["HPC_Ant"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_HPC_Med", "DMN_HPC_Med","FFA_HPC_Med")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["HPC_Med"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_HPC_Post", "DMN_HPC_Post","FFA_HPC_Post")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["HPC_Post"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_FFA", "DMN_FFA","HPC_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["FFA"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_PFC_FPCN_Par", "FPCN_PFC_DMN", "FPCN_PFC_HPC", "FPCN_PFC_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["FPCN_PFC"]]
suj_avg_list[[cond]] %>%
melt() %>%
filter(variable %in% c("FPCN_PFC_FPCN_Par", "FPCN_Par_DMN", "FPCN_Par_HPC", "FPCN_Par_FFA")) %>%
group_by(level, variable) %>%
summarise(average = mean(value, na.rm=TRUE), se_val = se(value), se_min = average-se_val, se_max = average+se_val) %>%
mutate(level = factor(level, levels = c("low", "med", "high"))) %>%
ggplot(aes(x=variable, y = average, fill=level)) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(aes(ymin = se_min, ymax = se_max), width=0.1, position = position_dodge(0.9))+
xlab("Regions")+
ylab("Average Connectivity")+
ggtitle(paste(names(suj_avg_list)[cond]))+
theme_classic()+
theme(aspect.ratio=1, axis.text.x = element_text(angle = 45, vjust = 0.5)) -> cond_list[["FPCN_Par"]]
plot_list_load_effect[[names(suj_avg_list)[cond]]] <- cond_list
}
plot_list_load_effect[["cue_load_effect"]][["within"]] + plot_list_load_effect[["delay_load_effect"]][["within"]] + plot_list_load_effect[["probe_load_effect"]][["within"]] +
plot_layout(guides="collect")+
plot_annotation(title = "Within Network Connectivity")
plot_list_load_effect[["cue_load_effect"]][["FPCN"]] + plot_list_load_effect[["delay_load_effect"]][["FPCN"]] + plot_list_load_effect[["probe_load_effect"]][["FPCN"]] +
plot_layout(guides="collect")+
plot_annotation(title = "FPCN Connectivity")
plot_list_load_effect[["cue_load_effect"]][["FPCN_PFC"]] + plot_list_load_effect[["delay_load_effect"]][["FPCN_PFC"]] + plot_list_load_effect[["probe_load_effect"]][["FPCN_PFC"]] +
plot_layout(guides="collect")+
plot_annotation(title = "FPCN PFC regions Connectivity")
plot_list_load_effect[["cue_load_effect"]][["FPCN_Par"]] + plot_list_load_effect[["delay_load_effect"]][["FPCN_Par"]]+ plot_list_load_effect[["probe_load_effect"]][["FPCN_Par"]] +
plot_layout(guides="collect")+
plot_annotation(title = "FPCN parietal regions Connectivity")
plot_list_load_effect[["cue_load_effect"]][["DMN"]] + plot_list_load_effect[["delay_load_effect"]][["DMN"]] +
plot_list_load_effect[["probe_load_effect"]][["DMN"]] +
plot_layout(guides="collect")+
plot_annotation(title = "DMN Connectivity")
plot_list_load_effect[["cue_load_effect"]][["HPC"]] + plot_list_load_effect[["delay_load_effect"]][["HPC"]] +
plot_list_load_effect[["probe_load_effect"]][["HPC"]] +
plot_layout(guides="collect")+
plot_annotation(title = "HPC Connectivity")
plot_list_load_effect[["cue_load_effect"]][["HPC_Ant"]] + plot_list_load_effect[["delay_load_effect"]][["HPC_Ant"]] +
plot_list_load_effect[["probe_load_effect"]][["HPC_Ant"]] +
plot_layout(guides="collect")+
plot_annotation(title = "Anterior HPC Connectivity")
plot_list_load_effect[["cue_load_effect"]][["HPC_Med"]] + plot_list_load_effect[["delay_load_effect"]][["HPC_Med"]] +
plot_list_load_effect[["probe_load_effect"]][["HPC_Med"]] +
plot_layout(guides="collect")+
plot_annotation(title = "Medial HPC Connectivity")
plot_list_load_effect[["cue_load_effect"]][["HPC_Post"]] + plot_list_load_effect[["delay_load_effect"]][["HPC_Post"]] +
plot_list_load_effect[["probe_load_effect"]][["HPC_Post"]] +
plot_layout(guides="collect")+
plot_annotation(title = "Posterior HPC Connectivity")
plot_list_load_effect[["cue_load_effect"]][["FFA"]] + plot_list_load_effect[["delay_load_effect"]][["FFA"]] +
plot_list_load_effect[["probe_load_effect"]][["FFA"]] +
plot_layout(guides="collect")+
plot_annotation(title = "FFA Connectivity")
# for (cond in seq.int(7,9)){
# print(names(suj_avg_list)[cond])
# anova_results <- purrr::map(suj_avg_list[[cond]][,2:34], ~aov(.x ~ suj_avg_list[[cond]]$level ))
# for (measure in seq.int(1,33)){
# print(colnames(suj_avg_list[[cond]])[measure+1])
# print(summary(anova_results[[measure]]))
# }
# }
print(TukeyHSD(aov(HPC_HPC ~ level, data = suj_avg_list[["cue_load_effect"]])))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = HPC_HPC ~ level, data = suj_avg_list[["cue_load_effect"]])
##
## $level
## diff lwr upr p adj
## low-high -0.2688836 -0.6587372 0.12097007 0.2353652
## med-high -0.4401530 -0.8300067 -0.05029939 0.0225964
## med-low -0.1712695 -0.5593307 0.21679175 0.5502562print(TukeyHSD(aov(DMN_FFA ~ level, data = suj_avg_list[["delay_load_effect"]])))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DMN_FFA ~ level, data = suj_avg_list[["delay_load_effect"]])
##
## $level
## diff lwr upr p adj
## low-high 0.05706789 -0.06134706 0.17548284 0.4909489
## med-high -0.10424375 -0.22321057 0.01472308 0.0987491
## med-low -0.16131164 -0.28080821 -0.04181507 0.0047901print(TukeyHSD(aov(HPC_FFA ~ level, data = suj_avg_list[["delay_load_effect"]])))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = HPC_FFA ~ level, data = suj_avg_list[["delay_load_effect"]])
##
## $level
## diff lwr upr p adj
## low-high 0.07625153 -0.04540013 0.19790320 0.3019064
## med-high -0.07714449 -0.19879615 0.04450718 0.2936061
## med-low -0.15339602 -0.27504769 -0.03174435 0.0092029print(TukeyHSD(aov(FPCN_Par_DMN ~ level, data = suj_avg_list[["delay_load_effect"]])))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_Par_DMN ~ level, data = suj_avg_list[["delay_load_effect"]])
##
## $level
## diff lwr upr p adj
## low-high 0.01280086 -0.0728181 0.098419821 0.9333968
## med-high -0.07860473 -0.1642237 0.007014234 0.0791664
## med-low -0.09140559 -0.1774164 -0.005394774 0.0343488print(TukeyHSD(aov(FFA_HPC_Ant ~ level, data = suj_avg_list[["delay_load_effect"]])))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FFA_HPC_Ant ~ level, data = suj_avg_list[["delay_load_effect"]])
##
## $level
## diff lwr upr p adj
## low-high 0.14176626 -0.0002304109 0.28376293 0.0504746
## med-high -0.04855659 -0.1905532566 0.09344008 0.6980985
## med-low -0.19032285 -0.3329577051 -0.04768799 0.0053828print(TukeyHSD(aov(FPCN_Par_FFA ~ level, data = suj_avg_list[["probe_load_effect"]])))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_Par_FFA ~ level, data = suj_avg_list[["probe_load_effect"]])
##
## $level
## diff lwr upr p adj
## low-high -0.11871513 -0.23785569 0.0004254353 0.0510489
## med-high -0.01755533 -0.13785665 0.1027459884 0.9364381
## med-low 0.10115980 -0.02020943 0.2225290201 0.1225360print(TukeyHSD(aov(FFA_HPC_Post ~ level, data = suj_avg_list[["probe_load_effect"]])))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FFA_HPC_Post ~ level, data = suj_avg_list[["probe_load_effect"]])
##
## $level
## diff lwr upr p adj
## low-high -0.19027688 -0.3602932 -0.02026053 0.0240834
## med-high -0.14189745 -0.3143703 0.03057540 0.1291917
## med-low 0.04837943 -0.1248467 0.22160557 0.7865541Correlation of load effect with omnibus span
Another way of looking at linear relationships is to look for correlations between connectivity and span. We see significant correlations between span and within FPCN PFC regions and FPCN parietal regions/DMN connectivity at cue, FFA/anterior HPC in delay (which is a negative correlation) and FPCN/FFA (specifically PFC, though trending towards Par), FPCN/Medial HPC, FFA/posterior HPC in probe.
# for (cond in seq.int(7,9)){
# print(names(suj_avg_list)[cond])
#
# for (measure in seq.int(2, 34)){
# print(colnames(suj_avg_list[[cond]])[measure])
# print(cor.test(suj_avg_list[[cond]][,measure], suj_avg_list[[cond]]$omnibus_span_no_DFR))
#
# }
# }
cor.test(suj_avg_list[[7]]$FPCN_PFC_FPCN_PFC, suj_avg_list[[cond]]$omnibus_span_no_DFR)##
## Pearson's product-moment correlation
##
## data: suj_avg_list[[7]]$FPCN_PFC_FPCN_PFC and suj_avg_list[[cond]]$omnibus_span_no_DFR
## t = 2.3792, df = 161, p-value = 0.01852
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03146259 0.32870037
## sample estimates:
## cor
## 0.1842918ggplot(data = suj_avg_list[[7]], aes(x=omnibus_span_no_DFR, y = FPCN_PFC_FPCN_PFC))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing missing values (geom_point).
cor.test(suj_avg_list[[7]]$FPCN_Par_DMN, suj_avg_list[[cond]]$omnibus_span_no_DFR)##
## Pearson's product-moment correlation
##
## data: suj_avg_list[[7]]$FPCN_Par_DMN and suj_avg_list[[cond]]$omnibus_span_no_DFR
## t = 1.7872, df = 161, p-value = 0.07578
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01455707 0.28704177
## sample estimates:
## cor
## 0.1394755ggplot(data = suj_avg_list[[7]], aes(x=omnibus_span_no_DFR, y = FPCN_Par_DMN))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 4 rows containing non-finite values (stat_smooth).
## Warning: Removed 4 rows containing missing values (geom_point).
cor.test(suj_avg_list[[8]]$FFA_HPC_Ant, suj_avg_list[[cond]]$omnibus_span_no_DFR)##
## Pearson's product-moment correlation
##
## data: suj_avg_list[[8]]$FFA_HPC_Ant and suj_avg_list[[cond]]$omnibus_span_no_DFR
## t = -2.1602, df = 164, p-value = 0.03221
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3107841 -0.0143797
## sample estimates:
## cor
## -0.1663369ggplot(data = suj_avg_list[[8]], aes(x=omnibus_span_no_DFR, y = FFA_HPC_Ant))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
cor.test(suj_avg_list[[9]]$FPCN_FFA, suj_avg_list[[cond]]$omnibus_span_no_DFR)##
## Pearson's product-moment correlation
##
## data: suj_avg_list[[9]]$FPCN_FFA and suj_avg_list[[cond]]$omnibus_span_no_DFR
## t = 2.1452, df = 161, p-value = 0.03344
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.01332075 0.31241470
## sample estimates:
## cor
## 0.1666999ggplot(data = suj_avg_list[[9]], aes(x=omnibus_span_no_DFR, y = FPCN_FFA))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing missing values (geom_point).
cor.test(suj_avg_list[[9]]$FPCN_PFC_FFA, suj_avg_list[[cond]]$omnibus_span_no_DFR)##
## Pearson's product-moment correlation
##
## data: suj_avg_list[[9]]$FPCN_PFC_FFA and suj_avg_list[[cond]]$omnibus_span_no_DFR
## t = 2.0223, df = 161, p-value = 0.0448
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.003761035 0.303761765
## sample estimates:
## cor
## 0.1573905ggplot(data = suj_avg_list[[9]], aes(x=omnibus_span_no_DFR, y = FPCN_PFC_FFA))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 4 rows containing non-finite values (stat_smooth).
## Warning: Removed 4 rows containing missing values (geom_point).
cor.test(suj_avg_list[[9]]$FPCN_Par_FFA, suj_avg_list[[cond]]$omnibus_span_no_DFR)##
## Pearson's product-moment correlation
##
## data: suj_avg_list[[9]]$FPCN_Par_FFA and suj_avg_list[[cond]]$omnibus_span_no_DFR
## t = 1.9647, df = 160, p-value = 0.05119
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.0007331023 0.3005620960
## sample estimates:
## cor
## 0.1534797ggplot(data = suj_avg_list[[9]], aes(x=omnibus_span_no_DFR, y = FPCN_Par_FFA))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 5 rows containing non-finite values (stat_smooth).## Warning: Removed 5 rows containing missing values (geom_point).
cor.test(suj_avg_list[[9]]$FPCN_HPC_Med, suj_avg_list[[cond]]$omnibus_span_no_DFR)##
## Pearson's product-moment correlation
##
## data: suj_avg_list[[9]]$FPCN_HPC_Med and suj_avg_list[[cond]]$omnibus_span_no_DFR
## t = 1.9462, df = 162, p-value = 0.05336
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.002147489 0.297510485
## sample estimates:
## cor
## 0.1511521ggplot(data = suj_avg_list[[9]], aes(x=omnibus_span_no_DFR, y = FPCN_HPC_Med))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).
cor.test(suj_avg_list[[9]]$FFA_HPC_Post, suj_avg_list[[cond]]$omnibus_span_no_DFR)##
## Pearson's product-moment correlation
##
## data: suj_avg_list[[9]]$FFA_HPC_Post and suj_avg_list[[cond]]$omnibus_span_no_DFR
## t = 3.0779, df = 161, p-value = 0.002451
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0850984 0.3758363
## sample estimates:
## cor
## 0.2357351ggplot(data = suj_avg_list[[9]], aes(x=omnibus_span_no_DFR, y = FFA_HPC_Post))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing missing values (geom_point).